similar to: Interpreting factor*numeric interaction coefficients

Hi, I am having some difficulties with matrix operations. It is a little hard to explain it so please bear with me. I have a very large data set, large enough that it needs to be split in parts in order to deal with. I can work things on these "parts" but the problem lies in adding together these parts for the final answer. So that been said, let's say that i split the data in 2

Dear list, I have a vector (array, table row, whatever is best) of frequency values for categories (or bins), and I need to find the median category. Trivial to do by hand, but I was wondering if there is a means to do it in R in an elegant way. The obvious medioan(vector) returns the median frequency for the binns, and that is not what I want. i.e,: freq cat1 1 cat2 10

I need to apply a yearly inflation factor to some wages and supply some simple sums by work category. I have gone at it with a brute force "for" loop approach which seems okay as it is a small dataset. It looks a bit inelegant and given all the warnings in the Intro to R, etc, about using loops I wondered if anyone could suggest something a bit simpler or more efficent? Example:

Hello R help I have a dataframe, with 71 samples (rows) and 30 variables. I got linear models for some of the variables,? and I want to join fitted and residuals of these models to the data frame. Sometimes, these vectors have the same length of the dependant variable, but in a few cases, NA values can be found on my data, and therefore, both fitted and residuals have a few rows less than the

I have a categorical variable in a dataframe similar to the following... cat 1 1 3 2 4 I need to convert it to 4 dichotemous variables for each observations like... cat1 cat2 cat3 cat4 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 Thanks in advance! Shane

i am making a category tree and i iterate over the category using my counter_cache however it would show a different number than what was actually being represetned in the tree. Here is an example cat.children = [cat2, cat3] puts cat.children.size 2 puts cat.children_count 2 cat4.parent_id = cat.id cat4.save puts cat.children.size

Hi, I am comparing the observed and fitted values of my GAM model, which includes the explanatory variables: longitude, depth, ssh, year and month. When I compare observed and fitted values for longitude, depth and ssh it works. But when I try to do it for month and year (which are as factors in the GAM model) it doesn't work. My observed and fitted values are exactly the same.. How is that

Hi all, I have some data as follows. Cat1 Cat2 Cat3 COG Counts A B C COG1 10 B D COG2 20 C COG3 30 D COG4 40 I would like to sum all the counts for each category: A B C D 10 30 40 60 >CAT2COG<-

Hi all, First time poster, so sorry if I commit some breech of posting etiquette. My problem is as follows. I have a data frame where each column represents a category and the individual data points in each category are binary responses (in this case they are actually 1's and 0's). What I want to extract are the counts for each category and put them in a vector. To do that I used the

Hi all, I have two prodction servers with FreeBSD 5.4 (all security patches are applied). They running some services like dns, ssh, http, ftp, etc. But I woukd like to encrypt some services for some hosts with ipsec when it is accessed. For example: - DNS resolution: not encrypted. - DNS replication master-slave: encrypted by ipsec. - Telnet: encrypted by ipsec for some hosts. Deny

Hi, I have a dataset with 6 categorical variables. I have used this following code to make the variables u1-u6 ordered factors and this works well. cat1cat2 cat3 cat4 cat5 cat6 ? 0 ? ?? 1 ? ? 1????? 0 ??? 0? ?? 1 ? 1 ? ?? 1 ? ? 0 ? ?? 0 ? ? 0 ? ? 0 ....... .... ############ data<-read,table("example.txt") data <- as.data.frame(lapply(data, ordered)) ############ Now,

I''m about to embark on a project and am seeking advice on my approach. I have an automotive site that needs to have shared categories. I''ve checked out different nested set, ancestral, acts_as_tree, acyclic plugins but feel they don''t exactly fit. So I am about to roll my own simple solution. Any feedback as to whether this looks good is appreciated. Essentially I

Hello, I can get the median for each factor, but I'd like another column to go with each factor. The nm column is a long name for the lvls column. So unique work except for the order can get messed up. Example: x =

Hello, I'm looking for a solution for the following problem: 1) I have a folder with several csv files; each contains a set of measurement values 2) The measurements of each file belong to a position in a two dimensional matrix (lets say "B02.csv" belongs to position 2,2 3) The size of the matrix is fix 4) I cannot assure to have a csv file for each position 5) Each position

I'm staring at an RFP--this company wants to replace a 2000 position PBX (at eight locations) with a new system. Their mindset is Nortel/Avaya because they talk about 28-button digital sets. The do specify a few IP phones for just one location, so they are aware of VoIP. I'm going to bid on this--there's nothing to lose except the time it takes to write the proposal. I'll

Dear list, I'm currently trying to use the rms package to get predicted ordinal responses from a conditional ratio model. As you will see below, my model seems to fit well to the data, however, I'm having trouble getting predicted mean (or fitted) ordinal response values using the predict function. I have a feeling I'm missing something simple, however I haven't been able to

Hi, I have two data sets; one is a population and the other a sample of that population. I am trying to plot both on the same trellis display. # Example data set with two numerical attributes and two categorical pop <- data.frame(var1=rnorm(2000, 2000, 500), var2=rnorm(2000, 2000, 500)) cat<-(runif(2000)<=.5)+0 for(i in 1:length(cat)){ if(cat[i] == 0){ pop[i,"cat1"] =

Hello all, I have been meaning to learn R for a while and have just subscribed to this list. I am planning to give R a shot at one of my live projects. I am looking to explore graphical features of R on my data below. Sample Data: Cat1 - Cat2 - Cat3 - Cat4 - NumPeople - Salary H - L - H - L - 100 - 50000 L - L - L - L - 40 - 30000 - H - H - - 100 - 45000 Cat1 through Cat4 are

Hi, I have the following dataset and I would like to create a dotplot with confidence limits: CAT1 CAT2 MEAN Lower Upper 1 1 1 0.619 0.392 0.845 2 1 10 1.774 1.030 2.518 3 1

Dear list, I've been using plotmeans {gplots} a lot before, and found the "connect" argument to be quite useful. I've moved to ggplot for several reasons, but would still like to connect lines conditionally, somehow. Is it possible to do? Small example: df <- data.frame(cat=LETTERS[1:4],num=rnorm(4)) ggplot(df, aes(x=cat, y=num)) + geom_point() +